AI is the technology trend of the moment—but what’s 10 years down the road? Some experts say quantum computing will be the next game changer. WSJ visited IBM’s quantum computing lab to learn why.
Category: robotics/AI – Page 338
The recent collaboration between Kaffa Roastery and Elev highlights the potential of AI in food science, particularly in creating personalized taste experiences.
Taste Buds Meet TechnologyThe recent collaboration between Kaffa Roastery and Elev, a Finnish AI consultancy, has shed light on the potential of artificial intelligence in the world of food science. By leveraging AI models to create a unique coffee blend tailored to enthusiasts’ tastes, this partnership has opened up a new realm of possibilities for personalized food experiences. But what if AI could go beyond just coffee blends? Imagine a future where AI can help individuals discover their unique taste profiles and create recipes specifically designed for their palates. This new approach to cooking could transform the way we think about food and our relationship with it.
A US company has built a flame-throwing robot dog capable f shooting jets of fire up to 30 feet that is available for purchase online.
Called the Thermonator, the four-legged robot comes equipped with an ARC Flamethrower mounted on its back.
Designed by Ohio-based firm Throwflame, the $9,420 (£7,600) robot is not advertised as a weapon, with the manufacturer suggesting possible uses include wildlife control, snow and ice removal and general entertainment.
#usa #dog #robot
Humanoid robots have been in development for many years by Japan’s Honda and Hyundai Motor’s Boston Dynamics. Earlier this year, Microsoft and Nvidia-backed startup Figure said it had signed a partnership with German automaker BMW to deploy humanoid robots in the car maker’s facility in the US.
Elon Musk said before that robot sales could become a larger part of the Tesla business. He said, “I think Tesla is best positioned of any humanoid robot maker to be able to reach volume production with efficient inference on the robot itself.”
We’re beginning to see the early stages of that trend pick up pace: In 2022, 34% of job tasks were completed by machines versus 66% by humans, according to the World Economic Forum’s “The Future of Jobs Report 2023”. By 2027, that ratio is expected to increase to 43% of tasks completed by machines and 57% by humans.
“On the one hand, yes, it’s scary to envision a world in which almost no job is safe from automation or from robotics. But the important thing to keep in mind is that through this kind of creative destruction process, while jobs will certainly be lost in some areas, there also will be jobs that will be gained.”
Despite those concerns, investors are looking for ways to bet on the growth of robotics. And according to the International Federation of Robotics, they don’t have to look very far. The US is home to the most suppliers that manufacture service robots and is well-positioned to cater to the rapidly growing global demand for robotics. The annual installation of industrial robots is expected to grow by about 30%, from 553,000 installations in 2022 to 718,000 in 2026.
Google CEO Sundar Pichai has admitted that the generative AI boom caught Google by surprise.
During an event at Stanford University earlier this month, the tech boss said his company was “surprised” by the sudden public interest in AI.
Despite saying he recognized the tech’s significance years ago, he admitted he had a “different sense of the trajectory in mind” when it came to society’s adoption of AI.
That led the Microsoft Research machine learning expert to wonder how much an AI model could learn using only words a 4-year-old could understand – and ultimately to an innovative training approach that’s produced a new class of more capable small language models that promises to make AI more accessible to more people.
Large language models (LLMs) have created exciting new opportunities to be more productive and creative using AI. But their size means they can require significant computing resources to operate.
While those models will still be the gold standard for solving many types of complex tasks, Microsoft has been developing a series of small language models (SLMs) that offer many of the same capabilities found in LLMs but are smaller in size and are trained on smaller amounts of data.
Researchers at Quebec AI Institute (Mila) have released, a technique that can turn any decoder-only LLM into a universal embedding model.
If you’re considering how your organization can use this revolutionary technology, one of the choices that have to be made is whether to go with open-source or closed-source (proprietary) tools, models and algorithms.
Why is this decision important? Well, each option offers advantages and disadvantages when it comes to customization, scalability, support and security.
In this article, we’ll explore the key differences as well as the pros and cons of each approach, as well as explain the factors that need to be considered when deciding which is right for your organization.